Data Feature Extraction Research Articles

An optimisation method for coplanar array capacitor image reconstruction based on Chambolle-Pock framework with automatic parameter selection

ABSTRACT Coplanar array capacitance imaging is an attractive imaging technique in industrial and detection fields, but low-quality images reduce its practicality and reliability. The inverse problem is difficult to solve and the image reconstruction quality is low, in this paper, an auxiliary optimisation strategy is proposed to transform the inverse problem of image reconstruction into a general minimisation problem, combined with the threshold of automatic parameter selection for feature extraction of capacitive data. The Kullback-Leibler(KL) data error term is combined with the Total Variation (TV) semi-norm to form a new imaging model. Utilising the Chambolle-Pock framework, the image reconstruction problem is transformed into a general minimisation problem to obtain high-quality images. Capacitance data from a 3 × 4 coplanar array capacitance sensor is used for feature extraction, serving as the basis for automatic parameter selection in the auxiliary optimisation strategy. The proposed Chambolle-Pock framework with the support of the auxiliary optimisation strategy, validated through simulation and experiments, outperforms popular imaging algorithms in terms of performance. The novel automatic parameter selection optimization algorithm introduced in this paper enhances the accuracy of capacitor image reconstruction in coplanar arrays.

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accurate prediction and prioritization of highly immunogenic and optimized MHC-I and MHC-II T-cell epitopes for cancer vaccine development and immunotherapy. Utilizing a curated dataset of experimentally validated epitopes and employing sophisticated machine learning techniques, VaxOptiML features three models: epitope prediction from target sequences, personalized HLA typing, and prioritizationthe predicted epitopesbased on immunogenicity scores. Our rigorous data extraction, cleaning, and feature extraction processes, coupled with model building, yield exceptional accuracy, sensitivity, specificity, and F1 score, surpassing existing prediction methods. Comprehensive visual representations underscore VaxOptiML's robustness and efficacy in accelerating epitope discovery and vaccine design for cancer immunotherapy. Deployed via Streamlit for public use, VaxOptiML enhances accessibility and usability for researchers and clinicians, demonstrating significant potential in cancer immunotherapy.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.

Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet’s appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications.

Second Version on the Product Color Variation Management using Artificial Intelligence

This research explores using artificial intelligence (AI) for managing color variations in products to boost market performance by optimizing product aesthetics and aligning with consumer preferences. The study investigates a system that leverages AI, deep learning, and neural networks to analyze real-time consumer data, including product preferences, buying history, and sales history. An AI model was created to predict and modify product colors dynamically, aiming to maximize consumer appeal and engagement. The system workflow includes stages for data gathering, processing, feature extraction, model training, software integration, and testing. AI-driven interventions were evaluated through consumer satisfaction, sales metrics, and digital engagement analytics, illustrating the potential for AI to influence product design effectively. This research suggests a transformative best practice for consumer-centered marketing, where AI facilitates color customization aligned with evolving consumer trends. (Sasibhushan Rao Chanthati, 2022)

A novel soft sensor modeling method based on gated stacked target-supervised VAE with variable weights

The variational autoencoder (VAE) has garnered extensive attention in the field of soft sensor modeling due to its superior capabilities in probabilistic data description and feature extraction. However, a single-layer VAE is challenging to extract higher-level features in the face of strong nonlinear process data. This paper proposes a gated stacked target-supervised VAE with variable weights (W-GSTVAE) to improve the modeling prediction performance of VAE. First, a stacked VAE is employed to enhance the feature extraction capability. In the pretraining phase, to enhance the correlation between the features and the target variable, feature learning is guided by incorporating the prediction error of target values into the loss function as well as calculating the maximum information coefficient between input and target variables. Meanwhile, in the fine-tuning phase, to make full use of shallow features, gated linear units are used to integrate the output features of each layer, fully exploiting the information from all layers. Finally, the effectiveness and superiority of the proposed model is demonstrated through two real industrial cases.

Automatic Cardiac Pathology Recognition in Echocardiography Images using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine learning-compatible collection of annotated images which can be used in the training phase of any kind of machine learning-based framework, including deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors’ knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the efficacy of the HODMD algorithm and the superiority of the proposed system, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

Enhancing Data Privacy Protection and Feature Extraction in Secure Computing Using a Hash Tree and Skip Attention Mechanism

This paper addresses the critical challenge of secure computing in the context of deep learning, focusing on the pressing need for effective data privacy protection during transmission and storage, particularly in sensitive fields such as finance and healthcare. To tackle this issue, we propose a novel deep learning model that integrates a hash tree structure with a skip attention mechanism. The hash tree is employed to ensure data integrity and security, enabling the rapid verification of data changes, while the skip attention mechanism enhances computational efficiency by allowing the model to selectively focus on important features, thus minimizing unnecessary processing. The primary objective of our research is to develop a secure computing model that not only safeguards data privacy but also optimizes feature extraction capabilities. Our experimental results on the CIFAR-10 dataset demonstrate significant improvements over traditional models, achieving a precision of 0.94, a recall of 0.89, an accuracy of 0.92, and an F1-score of 0.91, notably outperforming standard self-attention and CBAM. Additionally, the visualization of results confirms that our approach effectively balances efficient feature extraction with robust data privacy protection. This research contributes a new framework for secure computing, addressing both the security and efficiency concerns prevalent in current methodologies.

Software Refactoring Network: An Improved Software Refactoring Prediction Framework Using Hybrid Networking‐Based Deep Learning Approach

ABSTRACTSoftware refactoring plays a vital role in maintaining and improving the quality of software systems. The software refactoring network aims to connect developers, researchers, and practitioners to share knowledge, best practices, and tools related to refactoring. However, the network faces various challenges, such as the complexity of software systems, the diversity of refactoring techniques, and the need for automated and intelligent solutions to assist developers in making refactoring decisions. By leveraging deep learning techniques, the software refactoring network can enhance the speed, accuracy, and relevance of refactoring suggestions, ultimately improving the overall quality and maintainability of software systems. So, in this paper, an advanced deep learning–based software refactoring framework is proposed. The suggested model performs three phases as (a) data collection, (b) feature extraction, and (c) prediction of software refactoring. Initially, the data is collected from ordinary datasets. Then, the collected data is fed to the feature extraction stage, where the source code, process, and ownership metrics of all refactored and non‐refactored data are retrieved for further processing. After that, the extracted features are predicted using Adaptive and Attentive Dilation Adopted Hybrid Network (AADHN) techniques, in which it is performed using Deep Temporal Context Networks (DTCN) with a Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Here, the parameters in the hybrid networking model are optimized with the help of Constant Integer Updated Golden Tortoise Beetle Optimizer (CIU‐GTBO) for improving the prediction process. Therefore, the accuracy of the developed algorithm has achieved for different datasets, whereas it shows the value of 96.41, 96.38, 96.38, 96.38, 96.41, 96.38, and 96.39 for antlr4, junit, mapdb, mcMMO, mct, oryx, and titan. Also, the precision of the developed model has shown the better performance of 96.38, 96.32, 96.37, 96.33, 96.35, 96.37, and 96.31 for the datasets like antlr4, junit, mapdb, mcMMO, mct, oryx, and titan.

Dual-feature enhanced hybrid convolutional network for imbalanced fault diagnosis of rolling bearings

Abstract Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, in practical applications, the normal operating state occupies most of the service life of equipment, and the occurrence probability of each kind of fault is different, leading to imbalanced data that significantly degrades the performance of the neural network. In order to solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) is proposed. Firstly, an impulse segment enhancement module (ISEM) is constructed to enhance impulse segment features in the raw data, helping the network to learn fault features more accurately. Then, a hybrid convolutional module (HCM) is designed to fully mine discriminant fault features of minority classes from imbalanced data. In addition, a feature-enhanced combinational pooling module (FCPM) is devised to guide the network to focus more on the critical features and maximize the retention of key features in dimensionality reduction operations, thereby reducing the influence of data imbalance on the classifier. Finally, two distinct datasets are used to verify DEHCNet. Experimental results show that this network has better diagnostic accuracy and robustness under conditions of data imbalance.

Research on stock prediction based on CED-PSO-StockNet time series model

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model’s generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction.

Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning

Bearing failures are very common in the industrial environment, requiring effective fault detection methods, which can be categorized into physics-based, knowledge-based and data-driven types. Data-driven methods are efficient in differentiating healthy conditions from faulty conditions by characterizing machine signals, involving stages of data acquisition, feature extraction, and condition determination. Traditionally, feature extraction and condition determination were manual, but advances in artificial intelligence and machine learning, especially deep learning, have automated this process. Although deep learning automatically learns the best features from the input data, the signal domain can influence the model's performance. Time and frequency domain representations are widely used in fault detection methodologies using vibration signals, while angular and order domains are more common in variable operating conditions, but direct use of these domains with deep learning is still rare in the literature. Considering this, this study evaluates a bearing fault detection methodology using vibration signals in different domains (time, frequency, angular, and order) under various rotational conditions. Three distinct approaches were tested to assess the effectiveness of these representations. The results indicated that the frequency domain representation had the best overall performance and the study concluded that the angular and order domains do not offer significant advantages compared to the frequency domain. Nonetheless, it is recommended to conduct a more in-depth analysis with more diverse datasets, especially those containing early-stage bearing fault signals.

Medical image recognition based on multilayer neural network

As a common accidental injury, accurate identification of burns and scalded injuries is of great significance to the development of treatment programs and prognosis assessment. Traditional identification methods are subjective and inaccurate, this paper proposes a burns and scalds grade identification technique based on multilayer neural network. The three-degree classification standard of burns and scalds is described, and the structure and principle of multilayer neural network are introduced, including the network structure composed of input layer, hidden layer and output layer, forward propagation, back propagation and training process. The implementation steps of burns and scald grade recognition technology based on multi-layer neural network are discussed in detail, such as data acquisition, image pre-processing, feature extraction, classifier design, model training and evaluation. Through experiments, the model is tested using a dataset containing 362 burns and scalds images, and the model achieves more than 90% accuracy on the test set with high accuracy and reliability. The multi-layer neural network model is used to classify the burn and scald data, which improves the diagnostic accuracy, shortens the delay time of the disease, reduces the pressure of professional doctors, and helps patients have a preliminary understanding of the degree of burn.

MAMSC: a semantic enhanced representation model for public opinion key node recognition based on multianchor mapping in semantic communities

PurposeInformation is presented in various modalities such as text and images, and it can quickly and widely spread on social networks and among the general public through key communication nodes involved in public opinion events. Therefore, by tracking and identifying key nodes of public opinion, we can determine the direction of public opinion evolution and timely and effectively control public opinion events or curb the spread of false information.Design/methodology/approachThis paper introduces a novel multimodal semantic enhanced representation based on multianchor mapping semantic community (MAMSC) for identifying key nodes in public opinion. MAMSC consists of four core components: multimodal data feature extraction module, feature vector dimensionality reduction module, semantic enhanced representation module and semantic community (SC) recognition module. On this basis, we combine the method of community discovery in complex networks to analyze the aggregation characteristics of different semantic anchors and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations.FindingsThe experimental results show that compared with its variants and the baseline models, the MAMSC model has better recognition accuracy. This study also provides more systematic, forward-looking and scientific decision-making support for controlling public opinion and curbing the spread of false information.Originality/valueWe creatively combine the construction of variant autoencoder with multianchor mapping to enhance semantic representation and construct a three-layer network module for public opinion node recognition in the SC with strong, medium and weak associations. On this basis, our constructed MAMSC model achieved the best results compared to the baseline models and ablation evaluation models, with a precision of 91.21%.

Oil well fault diagnosis method based on markov transition field and convolutional neural network-long short-term memory

Abstract The analysis of electrical parameters and fault diagnosis in oil wells is crucial in the petroleum industry. Traditional methods often struggle to accurately capture the characteristics of time-series data, resulting in inadequate diagnostic precision. This study investigates a novel approach to improving feature extraction and fault diagnosis of electrical parameter data from oil wells. Specifically, the paper proposes encoding oil well electrical parameter data into two-dimensional images using the Markov Transition Field (MTF) technique to generate an oil well fault dataset. By incorporating the Squeeze-and-Excitation (SE) attention mechanism, the enhanced Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model demonstrates more efficient feature extraction capabilities in oil well fault classification. Experimental results show that this method achieves a fault diagnosis accuracy of 96.7%, significantly surpassing the traditional method’s 92.7%.This research provides an effective solution for improving the precision of fault diagnosis in pumping units, thereby enhancing the safety and economic efficiency of oil well operations.

Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal Data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multi-modal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-calibre, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field.

Learning Engagement through Multimodal Data Representation: A Theory-Based Practical Framework

The current integration and innovation of new artificial intelligence and data science are imparting unprecedented impacts and opportunities for the quality of teaching and the digital transformation of education. Consequently, there is a growing focus on the "learning engagement". Learning engagement, which measures the extent of a learner's cognitive, emotional, and behavioral involvement, is a crucial indicator for assessing academic achievement, learning satisfaction, and the quality of education. However, with the advancement of educational informatization, single data sources and traditional evaluation methods are no longer effective in capturing the multidimensional aspects of learner engagement. Therefore, leveraging multimodal data to integrate visual, auditory, physiological, and behavioral information for comprehensively supporting and assessing learner engagement has become an urgent issue. This paper reviews the evolution of the concept and mechanisms of AI-enabled learning engagement. It elaborates on the research paradigms and technological frontiers from the perspectives of various supporting theories, including embodied cognition theory, educational neuroscience, and self-determination theory. A theoretical framework encompassing four key processes—data collection and feature extraction, algorithm design and training, evaluation and optimization, and application—is constructed. Furthermore, it proposes preventive measures for potential data ethics and security issues, aiming to provide valuable references for the multidimensional development of learners and future educational interventions.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Deep learning-based Doppler-spread target detection with attention mechanism

Abstract In radar systems, Doppler spread issues commonly arise in space small target detections when integration is adopted during a long coherent processing interval (CPI). Even with precise compensation for translational effects, phase variations introduced by changes in target observation attitudes (TOA) can still cause the target’s echo energy to spread across multiple Doppler cells. As the TOA undergoes several periods over a CPI, the echo energy disperses into equidistant Doppler cells within a range cell in the range-Doppler (RD) map, which poses challenges for traditional methods and results in degraded detection performance. Leveraging the convolutional neural network’s ability to autonomously extract data features, we propose a neural network based on the Doppler auto-correlation attention mechanism (DAAM) to enhance detection accuracy and precisely estimate target positions by exploiting the sparse characteristics of the target Doppler spectrum. Training and testing are conducted using an RD map dataset, and experimental results demonstrate the network’s advanced performance in both target detection and position estimation.